docstr: :param x: the input into the bijection
  :type x: torch.Tensor
  
  Invokes the bijection x=>y; in the prototypical context of a
  :class:`~pyro.distributions.TransformedDistribution` `x` is a sample from
  the base distribution (or the output of a previous transform)
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code: def _call(self, x):
      """
      :param x: the input into the bijection
      :type x: torch.Tensor
  
      Invokes the bijection x=>y; in the prototypical context of a
      :class:`~pyro.distributions.TransformedDistribution` `x` is a sample from
      the base distribution (or the output of a previous transform)
      """
  
      y = x
      u = self.u()
      for idx in range(u.size(-2)):
          projection = (u[..., idx, :] * y).sum(
              dim=-1, keepdim=True
          ) * u[..., idx, :]
          y = y - 2.0 * projection
      return y
  
imports: import math
  import warnings
  from functools import partial
  import torch
  import torch.nn
  from torch.distributions import Transform, constraints
  from pyro.nn import DenseNN
  from ..conditional import ConditionalTransformModule
  from ..torch_transform import TransformModule
  from ..util import copy_docs_from
label_desc: Use image node_id 3 for calling the ConditionedHouseholder obj's underlying member method code with example usage: obj._call(x) and returns: y